Metabonomic Characterization of The Low-grade Human Astrocytomas and Meningiomas Using Magic-angle Spinning 1H Nuclear Magnetic Resonance Spectroscopy and Principal Component Analysis
- VernacularTitle:高分辨魔角旋转核磁共振和主成分分析研究人类低级星形细胞瘤和脑膜瘤的代谢组特征
- Author:
Wenxue CHEN
;
Haiyan LOU
;
Hongping ZHANG
;
Xiu NIE
;
Yun XIANG
;
Yongxia YANG
;
Guangyao WU
;
Jianpin QI
;
Yong YUE
;
Hao LEI
;
Huiru TANG
;
Feng DENG
- Publication Type:Journal Article
- Keywords:
human brain tumor;
glioma;
astrocytomas;
meningioma;
high resolution magic-angle spinning (HRMAS) nuclearmagnetic resonance spectroscopy;
pattern recognition;
statistical analysis
- From:
Progress in Biochemistry and Biophysics
2008;35(10):1142-1153
- CountryChina
- Language:Chinese
-
Abstract:
Metabolic characteristics of 39 human brain tumor tissues, including 15 astrocytomas, 13 fibroblastic meningiomas and 11 transitional meningiomas from 39 individual patients, have been studied using high resolution magic-angle spinning (HRMAS) 1H NMR spectroscopy in conjunction with principal component analysis (PCA). With rich metabolite information, 1H NMR spectra showed that the tumor-tissuc metabonome was dominated by lipids, lactate, myo-inositol, ereatine, choline metabolites such as choline, phosphocholine and glycerophosphocholine, amino acids such as alanine, glutamate, glutamine, taurine, N-acetyl-aspartate and glutathione. PCA of the tumor NMR spectra clearly showed metabonomic differences between low-grade astrocytomas and meningiomas whereas such differences were more moderate between fibroblastic and transitional meningiomas. Compared with meningiomas, the low-grade astrocytomas had higher levels of glycerophosphocholine, phosphocholine, myo-inositol and creatine but lower levels of alanine, glutamate, glutamine, glutathione and taurine. The N-acetyl-aspartate level was low but detectable in low-grade astrocytomas whereas it was not detectable in meningiomas. It is concluded that tissue metabonomics technology consisting of HRMAS 1H NMR spectroscopy and multivariate data analysis (MVDA) offers a useful tool (1) for distinguishing different types of brain tumors, (2) for providing the metabolic information for human brain tumors, which are potentially useful for understanding biochemistry of tumor progression.
